In this paper we propose interaction-driven Markov games (IDMGs), a new model for multiagent decision making under uncertainty. IDMGs aim at describing multiagent decision problem...
Various ways for decision making with imprecise probabilities—admissibility, maximal expected utility, maximality, E-admissibility, Γ-maximax, Γ-maximin, all of which are well...
We use the performance of the Bayesian ideal observer as a figure of merit for hardware optimization because this observer makes optimal use of signal-detection information. Due t...
Subok Park, Matthew A. Kupinski, Eric Clarkson, Ha...
Abstract. We present a method for using aligned ontologies to merge taxonomically organized data sets that have apparently compatible schemas, but potentially different semantics f...
In many real-life situations, we only have partial information about the actual probability distribution. For example, under Dempster-Shafer uncertainty, we only know the masses m...